System and method for continuous failure prediction and remediation within a computational environment using independent execution units

ABSTRACT

A system includes a computer system, a memory, and processor. The computer system includes a plurality of units of system resources, each executing a workload unit. The memory stores a set of remediation scripts. Each remediation script is associated with a known failure of a set of known failures within the computer system. Each remediation script is configured, when executed, to remediate the known failure. The processor measures performance metrics associated with the computer system. The processor determines, based on the performance metrics, that a probability that a failure within the computer system will occur within a future time is greater than a set threshold. In response, the processor determines, based on the values for the set of metrics, that the failure is a known failure, and executes a remediation script associated with the known failure.

TECHNICAL FIELD

The present disclosure relates generally to error detection/correctionand fault detection/recovery, and more particularly, to a system andmethod for continuous failure prediction and remediation within acomputational environment using independent execution units.

BACKGROUND

Many organizations rely on software systems to support their regularoperations. Failures within such systems are thus highly undesirable asthey may result in interrupted processes, data loss, and haltedoperations. In an attempt to avoid such failures, organizations oftenprovision their software systems with redundant computational resources,which may take over control of software execution within the system,when an issue occurs within the primary resources. However, becausecomputational resources are finite, if there is an inherent problem inthe software execution and/or computational infrastructure, itself, anyamount of redundancy provided will eventually run out.

SUMMARY

This disclosure contemplates a failure identification and preventiontool that is configured to automatically remediate potential failureswithin a computer system, before such failures materialize. Inparticular, the tool uses a pair of machine learning algorithms tocontinuously monitor and adjust the environment of the computer systemenvironment to prevent system failures. The first machine learningalgorithm is designed to identify potential failures, while the secondmachine learning algorithm is designed to adjust the computer systemenvironment to prevent the potential failures from occurring. This isaccomplished by dividing the resources of the computer system (e.g.,processing resources, memory resources, etc.) and the workload executingon the computer system into a set of independent execution units, eachprovided with a level of redundancy within the environment. Performancemetrics may then be measured for each independent execution unit, inorder to identify potential failures, and resets of the independentexecution units (during which active execution of the workload unit istransferred to a redundant unit of system resources) may be performed toautomatically prevent an identified potential failure. By automaticallyidentifying and preventing failures of the computer system, certainembodiments of the tool conserve the computational resources that wouldotherwise be wasted as a result of such failures. Certain embodiments ofthe tool are described below.

According to an embodiment, a system includes a computer system, amemory, and a hardware processor communicatively coupled to the memory.The computer system includes system resources that execute a workload.The workload is divided into a plurality of workload units. The computersystem includes a plurality of units of system resources. Each unit ofsystem resources of the plurality of units of system resources executesa workload unit of the plurality of workload units. The memory stores aset of remediation scripts. Each remediation script is associated with aknown failure of a set of known failures within the computer system.Each remediation script is configured, when executed, to remediate theknown failure. The hardware processor measures first values for a set ofperformance metrics associated with the computer system. The set ofperformance metrics includes, for each unit of system resources of theplurality of units of system resources, one or more metrics associatedwith a performance of the unit of system resources and one or moremetrics associated with a performance of the workload unit executing onthe unit of system resources. The hardware processor also determines,based on the first values for the set of performance metrics, that afirst probability that a first failure within the computer system willoccur within a first future time is greater than a set threshold. Inresponse to determining that the first probability is greater than theset threshold, the processor additionally determines, based on the firstvalues for the set of metrics, that the first failure is a known failureof the set of known failures within the computer system. In response todetermining that the first failure is the known failure, the processorfurther executes the remediation script associated with the knownfailure.

According to another embodiment, a system includes a computer system, amemory, and a hardware processor communicatively coupled to the memory.The computer system includes system resources that execute a workload.The workload is divided into a plurality of workload units. The systemresources include a plurality of units of system resources including aplurality of active units of system resources and a plurality ofredundant units of system resources. Each workload unit of the pluralityof workload units is assigned to an active unit of system resources ofthe plurality of active units of system resources. The assigned activeunit of system resources executes the workload unit. Each redundant unitof system resources of the plurality of redundant units of systemresources is assigned to an active unit of system resources of theplurality of active units of system resources. The memory stores areinforcement learning algorithm that is configured to generate asequence of resets for execution within the computer system, based onvalues for a set of performance metrics associated with the computersystem. Each reset of the sequence of resets is associated with aparticular workload unit executing on a given active unit of systemresources of the plurality of active units of system resources.Executing the reset includes exchanging the given active unit of systemresources with the redundant unit of system resources assigned to thegiven active unit of system resources, at least in part by transferringactive execution of the particular workload unit from the given activeunit of system resources to the redundant unit of system resourcesassigned to the given active unit of system resources. For each activeunit of system resources of the plurality of active units of systemresources, the set of performance metrics includes one or more metricsassociated with a performance of the active unit of system resources andone or more metrics associated with a performance of the workload unitexecuting on the active unit of system resources. The processor measuresthe values for the set of performance metrics associated with thecomputer system. The processor also determines, based on the values forthe set of performance metrics, that a first probability that a firstfailure within the computer system will occur within a first future timeis greater than a set threshold. In response to determining that thefirst probability is greater than the set threshold, the processorexecutes the reinforcement learning algorithm to generate the sequenceof resets for the computer system. The processor also executes eachreset of the sequence of resets. The processor additionally measures newvalues for the set of performance metrics associated with the computersystem. The processor also determines, based on the new values for theset of performance metrics, a second probability that the first failurewithin the computer system will occur within the first future time. Theprocessor further updates the reinforcement learning algorithm based ona difference between the first probability and the second probability.

The tool described in the present disclosure may be integrated into apractical application of a failure prevention tool, which monitors acomputational environment and proactively takes action to preventfailures from occurring within the environment, thereby avoidingunnecessary system downtime, data loss, and any other undesirableconsequences of a system failure. As used throughout this disclosure, afailure corresponds to any error or condition within a computer systemthat may cause the system (and/or application(s) executing on thesystem) to freeze and/or crash. For example, a failure may correspond toa memory leak, an improper distribution of computational resources(e.g., leading to a process attempting to use more CPU power, RAM,and/or storage than available to it), a memory access error (e.g., asegmentation fault), a buffer overflow error, and/or any othererror/condition that may cause the system (and/or application(s)executing on the computer system) to freeze and/or crash. Such failuresmay lead to unnecessary system and/or application downtime, during whichthe system may need to be rebooted and/or application(s) may need to berestarted. Furthermore, processes that were halted mid-execution mayneed to be restarted and data generated by such processes may be lost.Accordingly, by preventing failures from occurring within acomputational environment, certain embodiments may conserve thecomputational resources that would otherwise be expended on rebootingthe system and/or restarting applications running within the system.Furthermore, by automatically attempting to remediate even unknownfailures, for which remediation scripts have not yet been developed,certain embodiments of the tool may prevent or at least sufficientlydelay a potential failure from occurring within a computationalenvironment, such that a system administrator may address the potentialfailure before it occurs.

Certain embodiments may provide one or more technical advantages. As anexample, an embodiment conserves the computational resources that wouldotherwise be wasted as a result of a need to reboot the system after asystem crash caused by a system failure. In particular, certain suchembodiments may conserve the computational resources associated withreinitializing applications and processes within the system, which werehalted as a result of the system crash. As another example, anembodiment iteratively applies a pair of machine learning algorithms toboth identify potential failures within the system and to remediateidentified potential failures, thereby helping to prevent the system(and/or application(s) executing on the system) from crashing. Asanother example, an embodiment divides both the computational resourcesand the workload to be executed within a computational environment intoa set of smaller units, enabling the system performance to be probed ata fine grain scale. In this manner, the computational and/or workloadunits associated with a potential failure may be identified and resetsof those units may be performed. Thus, certain embodiments enableremediation of potential failures within the system in a computationallyefficient manner, without wasting the computational resources associatedwith a full system restart. As another example, an embodimentautomatically identifies and executes remediation scripts that havepreviously been developed to remediate known issues, without needing toinform a system administrator of an impending failure. In this manner,certain embodiments leverage prior knowledge of remediation methods toautomatically prevent system crashes from occurring where the system isable to do so, rather than relying on a system administrator who may notbe able to direct his/her attention to the identified issues before theymaterialize and cause the system to crash. As a further example, anembodiment automatically executes a sequence of resets of certainportions of computational resources within the computationalenvironment, in an attempt to automatically remediate an unknownpotential failure, thereby preventing the system from crashing. Thus,certain embodiments reduce the amount of time during which acomputational system is unavailable, and conserve the computationalresources otherwise associated with rebooting/restarting the systemafter a system crash. In particular, the embodiment uses a reinforcementlearning algorithm to learn which resets improve the system, therebypotentially avoiding or at least delaying system failures until they canotherwise be addressed.

Certain embodiments may include none, some, or all of the abovetechnical advantages. One or more other technical advantages may bereadily apparent to one skilled in the art form the figures,descriptions, and claims included herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure, referenceis now made to the following description, taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example failure identification and preventionsystem;

FIGS. 2A and 2B illustrate the manner by which the workload andcomputational resources of the system of FIG. 1 are divided into a setof independent execution units;

FIG. 3 presents a flowchart illustrating the manner by which the failureidentification and prevention tool of the system of FIG. 1 identifiesand prevents potential failures from occurring within the system; and

FIG. 4 presents a flowchart illustrating the manner by which the failureidentification and prevention tool of the system of FIG. 1 attempts toautomatically prevent an unknown potential failure within the system byexecuting resets within the execution units illustrated in FIGS. 2A and2B.

DETAILED DESCRIPTION

Embodiments of the present disclosure and its advantages may beunderstood by referring to FIGS. 1 through 4 of the drawings, likenumerals being used for like and corresponding parts of the variousdrawings.

System Overview

FIG. 1 illustrates an example failure identification and preventionsystem 100 that includes user(s) 104, device(s) 106, network 108,computational environment 110, database 124, and failure identificationand prevention tool 102. As illustrated in FIG. 1 , computationalenvironment 110 is associated with system resources that includeprocessing resources 112 and memory/storage resources 114. Together,processing resources 112 and memory/storage resources 114 are configuredto execute a computational workload that includes operating systemworkload 116, middleware workload 118, database workload 120, andapplication workload 122. In the context of this disclosure, a failurecorresponds to any unexpected/undesirable condition occurring withincomputational environment 110, which may cause downtown within theenvironment, thereby rendering the environment incapable of supportingall or a portion of its normal operations (e.g., incapable of executingall or a portion of operating system workload 116, middleware workload118, database workload 120, and/or application workload 122).

In general, failure identification and prevention tool 102 operates toprevent failures from occurring within computational environment 110 by:(1) monitoring a set of performance metrics for computationalenvironment 110, (2) predicting, based on the set of performancemetrics, a probability that a future failure within computationalenvironment will occur within a future time, (3) in response todetermining that the probability that the predicted failure will occurwithin the future time is greater than a threshold, classifying thepredicted failure as either (a) one of a set of known failures withinthe computational environment, or (b) an unknown failure, and (4)attempting to remediate the system to avoid the predicted failure, basedon the classification of the failure. In particular, in response toclassifying a predicted failure as a specific known failure, failureidentification and prevention tool 102 is configured to execute aremediation script 126, which has been designed to address the knownfailure, as described in further detail below and in the discussion ofFIG. 3 . In response to classifying a predicted failure as an unknownfailure, in certain embodiments, failure identification and preventiontool 102 is configured to transmit a request 136 to an administrator104, requesting that the administrator manually investigatecomputational environment 110 and attempt to remediate the predictedfailure, as described in further detail below and in the discussion ofFIG. 3 . In some embodiments, in response to classifying a predictedfailure as an unknown failure, failure identification and preventiontool 102 is configured to automatically attempt to prevent the failure,by executing a sequence of resets within computational environment 110,as described in further detail below and in the discussion of FIG. 4 .

Device(s) 106 are used by user(s) 104 (e.g., system administrators) tocommunicate with failure identification and prevention tool 102. Forexample, device 106 may be configured to receive messages 136 alertingadministrator 104 to unknown failures predicted to occur withincomputational environment 110. In the context of this disclosure, anunknown failure corresponds to any issue within computationalenvironment 110 for which a remediation script is not available withinthe set of remediation scripts 126. In certain embodiments, messages 136include information that may aid administrator 104 in remediatingcomputational environment 110 in order to prevent the unknown failures.For example, messages 136 may include a time-series of values for a setof performance metrics evaluated for computational environment 110. Suchperformance metrics may include metrics associated with usage ofcomputational resources (e.g., CPU, RAM, storage, etc.) withincomputational environment 110, metrics associated with errors, warnings,and/or alerts generated within computational environment 110 (e.g.,application error codes, page faults, etc.), and/or metrics associatedwith any other suitable performance parameters measured/observed withincomputational environment 110. In some embodiments, device 106 may beconfigured to display the performances metrics to user 104. For example,device 106 may be configured to display a dashboard in which theperformance metrics may be displayed.

In certain embodiments, devices 106 may be used by system administrators104 to transmit remediation scripts 138 to failure identification andprevention tool 102 for storage in database 124. For example, inresponse to receiving message 136 alerting administrator 104 to apredicted unknown failure within computational environment 110,administrator 104 may identify the cause of the predicted failure,remediate the cause, generate a script that may be executed in thefuture should a similar issue arise, and transmit the script to failureidentification and prevention tool 102 and/or database 124, for storagewithin database 124.

Devices 106 include any appropriate device for communicating withcomponents of system 100 over network 108. For example, devices 106 mayinclude a mobile phone, a computer, a laptop, a wireless or cellulartelephone, a tablet, a server, an IoT device, and/or an automatedassistant, among others. This disclosure contemplates devices 106 beingany appropriate device for sending and receiving information overnetwork 108, and/or displaying information (e.g., performance metrics)received from failure identification and prevention tool 102. In someembodiments, device 106 may include a display, a keypad, or otherappropriate terminal equipment usable by user 104. In some embodiments,an application executed by a processor of device 106 may perform thefunctions described herein.

Network 108 facilitates communications between components of system 100including, for example, failure identification and prevention tool 102,devices 106, computational environment 110, and database 126. Network108 may include any interconnecting systems capable of transmittingaudio, video, signals, data, messages, or any combination of thepreceding. For example, network 108 may include all or a portion of apublic switched telephone network (PSTN), a public data network, ametropolitan area network (MAN), a wide area network (WAN), a local,regional, or global communication or computer network, such as theInternet, a wireline or wireless network, or any other suitablecommunication link, including combinations thereof, operable tofacilitate communication between components of system 100.

Computational environment 110 corresponds to any computer system thatincludes a set of system resources (e.g., processing resources 112 andmemory resources 114), along with a workload executing on the systemresources. System resources (e.g., processing resources 112 and/ormemory resources 114) may include any suitable hardware and/or softwarecomponents configured to execute workload components 116 through 122. Asan example, system resources 112/114 may include an application server(e.g., J2EE, Glassfish, Apache Tomcat, Apache Geronimo, etc.), adatabase server (e.g., SQL, Oracle, DB2, etc.), and/or any othersuitable computational resources.

As illustrated in FIG. 1 , the workload configured to execute on systemresources 112/114 includes an operating system workload 116, amiddleware workload 118, a database workload 120, and an applicationworkload 122. Operating system workload 116 may be associated with anysuitable operating system software including, but not limited to,Microsoft Windows OS, Mac OS, Linux OS (e.g., Ubuntu, Fedora, Redhat,Mint, etc.), and/or any other suitable operating systems. Middlewareworkload 118 may be associated with database middleware installed on agiven database server, application server middleware installed on agiven application server, and/or any other suitable middleware. Databaseworkload 120 may include a set of SQL statements configured to executeon a given database server, and/or any other suitable database workload.Application workload 122 may include a set of tasks to be performed bythe applications configured to execute on the application servers withincomputational environment 110. While illustrated in FIG. 1 as includingoperating system workload 116, middleware workload 118, databaseworkload 120, and application workload 122, this disclosure contemplatesthat computational environment 110 may include any suitablecomputational workload configured to execute on the computationalresources included within the system.

As described in further detail below, both the system resources 112/114and computational workloads 116 through 122 of computational environment110 may be divided into units of system resources (referred tothroughout this disclosure as computational units) and workload units,respectively. Each computational unit corresponds to a portion of thesystem resources within the computational environment, and each workloadunit corresponds to a portion of the workload executing within thecomputational environment. FIG. 2A illustrates an embodiment in whichthe computational resources of computational environment 110 have beendivided into a set of twenty-four computational units 204 a through 204x, and the workload of computational environment 110 has been dividedinto a set of 12 workload units 206 through 228 (illustrated in FIG. 2Aas a first set of workload units 206 a through 228 a, and a second,redundant set of workload units 206 b through 228 b, the use of which isexplained in detail below). Each workload unit 206 a/b through 228 a/bis assigned to at least one computational unit 204 a through 204 x,which is configured to execute the assigned workload unit. In thismanner, performance metrics may be evaluated on a per computational unitbasis. For instance, the CPU usage and memory usage of computationalunit 204 a may be obtained independently from the CPU usage and memoryusage of computational unit 204 d. In certain embodiments, each workloadunit 206 is assigned one or more redundant computational units. Forexample, FIG. 2A illustrates an example in which each workload unit 206is assigned to a primary computational unit (illustrated in FIG. 2A asworkload units 206 a through 228 a executing on primary computationalunits 204 a through 204 l, and workload units 206 b through 228 bexecuting on redundant workload units 204 m through 204 x). Furtherdetails of the manner by which the computational resources 112/114 andworkloads 116 through 122 of computational environment 110 are dividedinto units, and the use of such units for failure prediction andremediation are described below, in the discussion of FIGS. 2A/2B and3-4.

Returning to FIG. 1 , database 124 is any storage location within system100 where remediation scripts 126 may be stored. For example, database124 may correspond to a database, a server, a local storage system, anexternal storage system, cloud storage, and/or any other suitablestorage location. Each remediation script 126 a through 126 n isassociated with a known failure within computational environment 110.This disclosure contemplates that a known failure is any failure that(1) has previously occurred within computational environment 110; (2)has previously been predicted as likely to occur within computationalenvironment 110; (3) has been recognized as potentially affectingcomputational environment 110; and/or (4) is similar enough to a failureof any of the preceding types, such that it makes sense to attempt toremediate it in the same manner. On the other hand, an unknown failureis any failure that cannot be classified as a known failure.

Each remediation script 126 a through 126 n is designed to prevent theassociated known failure from occurring within computational environment110. In particular, each remediation script 126 a through 126 n includesinstructions that, when executed within computational environment 110,are configured to adjust computational environment 110 (e.g., droppingcertain processes, restarting certain computational resources, etc.) soas to avoid the known failure.

As illustrated in FIG. 1 , failure identification and prevention tool102 includes processor 128 and memory 130. This disclosure contemplatesprocessor 128 and memory 130 being configured to perform any of thefunctions of failure identification and prevention tool 102 describedherein. Generally failure identification and prevention tool 102: (1)divides the computational resources (e.g., processing resources 112 andmemory resources 114) of computational environment 110 into a set ofcomputational units; (2) divides computational workloads 116 through 122into a set of workload units; (3) assigns each workload unit to aprimary computational unit and, in certain embodiments, to one or moreredundant computational units; (4) measures performance metrics for eachof the primary computational units and their assigned workload units;(5) predicts, based on the measured performance metrics, a probabilitythat a failure will occur at a future time within the computationalenvironment 110; (6) determines whether the predicted probability of afuture failure is greater than a threshold; (7) in response todetermining that the predicted probability of a future failure isgreater than the threshold, predicts whether the future failure is aknown failure, or an unknown failure; (8) in response to predicting thatthe future failure is a known failure, executes the remediation script126 associated with the known failure; (9) in response to determiningthat the future failure is an unknown failure: (a) requests manualremediation through a remediation request 136 transmitted to a systemadministrator 104, or (b) attempts to automatically prevent the unknownfailure, by generating and executing a sequence of resets withincomputational environment 110. The manner by which failureidentification and prevention tool 102 performs these functions isdescribed in detail below, in the discussion of FIGS. 2A/B, and 3-4.

Processor 128 is any electronic circuitry, including, but not limited tocentral processing units (CPUs), graphics processing units (GPUs),microprocessors, application specific integrated circuits (ASIC),application specific instruction set processor (ASIP), and/or statemachines, that communicatively couples to memory 130 and controls theoperation of failure identification and prevention tool 102. Processor128 may be 8-bit, 16-bit, 32-bit, 64-bit or of any other suitablearchitecture. Processor 128 may include an arithmetic logic unit (ALU)for performing arithmetic and logic operations, processor registers thatsupply operands to the ALU and store the results of ALU operations, anda control unit that fetches instructions from memory and executes themby directing the coordinated operations of the ALU, registers and othercomponents. Processor 128 may include other hardware and software thatoperates to control and process information. Processor 128 executessoftware stored on memory 130 to perform any of the functions describedherein. Processor 128 controls the operation and administration offailure identification and prevention tool 102 by processing informationreceived from device(s) 106, network 108, computational environment 110,database 124, and/or memory 130. Processor 128 may be a programmablelogic device, a microcontroller, a microprocessor, any suitableprocessing device, or any suitable combination of the preceding.Processor 128 is not limited to a single processing device and mayencompass multiple processing devices.

Memory 130 may store, either permanently or temporarily, data,operational software, or other information/instructions 132 forprocessor 128. Memory 130 may include any one or a combination ofvolatile or non-volatile local or remote devices suitable for storinginformation. For example, memory 130 may include random access memory(RAM), read only memory (ROM), magnetic storage devices, optical storagedevices, or any other suitable information storage device or acombination of these devices. The software represents any suitable setof instructions, logic, or code embodied in a computer-readable storagemedium. For example, the software may be embodied in memory 130, a disk,a CD, or a flash drive. In particular embodiments, the software mayinclude an application executable by processor 128 to perform one ormore of the functions described herein.

In certain embodiments, memory 130 may also store one or more machinelearning algorithms 134. For example, memory 130 may store a firstmachine learning algorithm 134 a that is configured to predict, based ona set of performance metrics measured within the computationalenvironment 110, a probability that a failure will occur within thecomputational environment within a future time. As an example, machinelearning algorithm 134 a may be configured to predict a probability offuture failure based on a set of performance metrics that includes, foreach computational unit actively executing a workload unit: (1) aperformance metric associated with a usage of the processing resources(e.g., CPU usage) of the computational unit; (2) a performance metricassociated with a memory usage (e.g., RAM usage) of the computationalunit; (3) a performance metric associated with any error codes generatedby the workload unit executing on the computational unit; (4) aperformance metric associated with any page faults experienced by thecomputational unit; and/or (5) any other suitable performance metrics.In particular, in certain embodiments, machine learning algorithm 134 amay be trained based on historical values for the performance metricsalong with knowledge of any historical failures experienced by and/orexpected for computational environment 110, to identify correlationsbetween the values for the performance metrics and potential failures.

Machine learning algorithm 134 a may be any suitable machine learningalgorithm trained to predict potential system failures based on a set ofperformance metrics measured for each of the active computational unitsexecuting workload units within computational environment 110. As anexample, in certain embodiments, machine learning algorithm 134 a is aneural network.

Memory 130 may also store a second machine learning algorithm 134 b thatis configured to operate in conjunction with first machine learningalgorithm 134 a. In particular, in response to machine learningalgorithm 134 a predicting that a failure is likely to occur withincomputational environment 110, second machine learning algorithm 134 bis configured to classify the potential failure as either one of anumber of known failures, or an unknown failure. Machine learningalgorithm 134 b may classify the potential failure based on the valuesfor the performance metrics measured for the set of computational unitsactively executing workload units within computational environment 110.

Machine learning algorithm 134 b may include any machine learningalgorithm trained to classify a potential failure as either one of anumber of known failures, or an unknown failure. For example, machinelearning algorithm 134 a may be a neural network, a k-nearest neighborsalgorithm, a decision tree algorithm, a naïve Bayes algorithm, a randomforest algorithm, a gradient boosting algorithm, and/or any othersuitable machine learning algorithm.

In certain embodiments, in response to classifying a potential failureas an unknown failure, machine learning algorithm 134 b may be furtherconfigured to automatically generate remediation instructions forexecution within computational environment 110. It may be desirable forfailure identification and prevention tool 102 to attempt toautomatically prevent an unknown failure from occurring withincomputational environment 110 rather than simply alerting a systemadministrator 104 to the potential failure, because it may take timefrom the system administrator to address the potential failure. Duringthat time, the potential failure may occur within the system.Accordingly, any attempts to prevent the failure from occurring arelikely preferable to inaction.

In certain embodiments, the remediation instructions generated by secondmachine learning algorithm 134 b may take the form of a sequence ofresets executed within computational environment 110. The manner inwhich machine learning algorithm 134 b generates and executes thesequence of resets is described in further detail below, in thediscussion of FIGS. 2A and 2B.

Second machine learning algorithm 134 b may include any suitable machinelearning algorithm configured to generate remediation instructions forexecution within computational environment 110, based on values of a setof performance metrics measured for the active computational unitsexecuting within computational environment 110. As an example, incertain embodiments, machine learning algorithm 134 b includes areinforcement learning algorithm. For instance, in certain embodiments,reinforcement learning algorithm 142 is a deep Q reinforcement learning(DQN) algorithm, a double deep Q reinforcement learning (DDQN)algorithm, a deep deterministic policy gradient (DDPG) algorithm, and/orany other suitable reinforcement learning algorithm. The reinforcementlearning algorithm may be trained to generate remediation instructionsfor execution within computational environment 110 in any suitablemanner. For example, in certain embodiments, in response to firstmachine learning algorithm 134 a predicting, based on values of a set ofperformance metrics measured for the active computational unitsexecuting within computational environment 110, that a probability thata potential failure will occur within computational environment 110 isgreater than a given threshold, the reinforcement learning algorithm isconfigured to generate remediation instructions based on thoseperformance metric values. Failure identification and prevention tool102 may then measure new values for the set of performance metrics, andapply the first machine learning algorithm 134 a to those new values, todetermine a new probability that a potential failure will occur withincomputation environment 110. If the new probability is lower than theoriginal probability, failure identification and prevention tool 102 mayreward the reinforcement learning algorithm. On the other hand, if thenew probability is greater than or equal to the original probability,failure identification and prevention tool 102 may punish thereinforcement learning algorithm.

Modifications, additions, or omissions may be made to the systemsdescribed herein without departing from the scope of the invention. Forexample, system 100 may include any number of users 104, devices 106,networks 108, computational environments 110, processing resources 112,memory resources 114, operating system workloads 116, middlewareworkloads 118, database workloads 120, application workloads 122,databases 124, remediation scripts 126, processors 128, memories 130,and/or machine learning algorithms 134 a/b. The components may beintegrated or separated. Moreover, the operations may be performed bymore, fewer, or other components. Additionally, the operations may beperformed using any suitable logic comprising software, hardware, and/orother logic.

II. Independent Execution Units

As explained above, failure identification and prevention tool 102 isconfigured to (1) divide the computation resources (e.g., processingresources 112 and memory resources 114) of computational environment 110into a set of computational units; and (2) divide the workload executingwithin computational environment 110 (e.g., operating system workload116, middleware workload 118, database workload 120, and applicationworkload 122) into a set of workload units. FIGS. 2A and 2B present anexample illustrating a set of computational units, each executing aworkload unit, as an independent execution unit.

FIG. 2A illustrates, for simplicity, an example in which thecomputational resources of computational environment 110 have beendivided into a set of twenty-four computational units 204 a through 204x, and the computational workload to be executed within computationalenvironment 110 has been divided into a set of 12 workload units 206through 228. In particular, the operation system workload 116 has beendivided into a set of three workload units 206 through 210, themiddleware workload 118 has been divided into a set of three workloadunits 212 through 216, the database workload 120 has been divided into aset of three workload units 218 through 222, and the applicationworkload 122 has been divided into a set of three workload units 224through 228. As illustrated in FIG. 2A, the computational resources ofcomputational environment 110 have been divided into twice as many unitsas has the workload to be executed within computational environment 110,providing a first level of redundancy for each workload unit. Inparticular, the computational resources within computational environment110 have been divided into a primary set of computational units 202 aand a redundant set of computational units 202 b. As illustrate in FIG.2A, for each workload unit 206 through 228, the primary set ofcomputational units 202 a includes computational units 204 a through 204l, each of which is configured to actively execute a first copy 206 athrough 228 a of a workload unit 206 through 228. Similarly, for eachworkload unit 206 through 228, the redundant set of computational units202 b includes computational units 204 m through 204 x, each of which isconfigured to passively execute a second copy 206 b through 228 b of aworkload unit 206 through 228. While illustrated as a single level ofredundancy in FIG. 2A, this disclosure contemplates that thecomputational resources of computational environment 110 may divided inany suitable manner, to provide any desired level of redundancy to theworkload units 206 through 228. Furthermore, this disclosurecontemplates that the workload executing within computationalenvironment 110 may divided into any suitable number of workload units206 through 228.

This disclosure contemplates that the redundancy provided by redundantcomputational units 204 m through 204 x may take any suitable form. Asan example, in certain embodiments, both the active computational units204 a through 204 l and the redundant computational units 204 m through204 x are configured to execute their copies of workload units 206through 228, but only the active computational units 204 a through 204 lare configured to generate results and/or serve user requests. Asanother example in some embodiments, only the active computational units204 a through 204 l execute workload units 206 through 228, and suchexecution may, at any point, be transferred to a redundant computationalunit 204 m through 204 x.

As explained above, in certain embodiments, second machine learningalgorithm 134 b is configured to automatically generate a set ofremediation instructions, in an attempt to prevent a potential failurepredicted by first machine learning algorithm 134 a. In some suchembodiments, the set of remediation instructions takes the form of asequence of primary computational unit resets, to be executed withincomputational environment 110. As an example, a sequence of primarycomputational unit resets may include (1) a reset of computational unit204 a, executing workload unit 206 a, (2) a reset of computation unit204 f, executing workload unit 216 a, (3) a reset of computational unit204 g, executing workload unit 218 a, and (4) a reset of computationalunit 204 k, executing workload unit 226 a. During each primarycomputational unit reset, active execution of the associated workloadunit is transferred to a corresponding redundant computational unit,execution of the workload unit is halted on the primary computationalunit, and a reset is performed on the primary computational unit. Inthis manner, the primary computational unit and the redundantcomputational unit exchange roles. FIG. 2B illustrates an example inwhich active execution of workload unit 206 (illustrated in FIGS. 2A and2B by shading of the computational unit) has been transferred fromprimary computational unit 204 a to redundant computational unit 204 m,active execution of workload unit 216 has been transferred from primarycomputational unit 204 f to redundant computation unit 204 r, activeexecution of workload unit 218 has been transferred from primarycomputational unit 204 g to redundant computational unit 204 s, andactive execution of workload unit 226 has been transferred from primarycomputational unit 204 k to redundant computational unit 204 w.

This disclosure contemplates that second machine learning algorithm 134b may be configured to generate any suitable sequence of resets, whichmay be executed in any suitable order within computational environment110. In certain embodiments, in response to generating a first sequenceof resets and executing the first sequence of resets withincomputational environment 110, second machine learning algorithm 134 bmay be configured to generate a second series of resets to executewithin the computational environment, based on the response of thecomputational environment to the first sequence of resets. For example,consider a situation in which the first machine learning algorithm 134 adetermines a probability P1 of potential failure, and second machinelearning algorithm 134 b generates a first sequence of resets that areexecuted within computational environment 110 in an attempt by failureidentification and prevention tool 102 to prevent the potential failurefrom occurring within the computational environment. If, after executingthe sequence of resets, failure identification and prevention tool 102determines that the computational environment has improved (e.g., a newprobability P2 of potential failure is less than the originalprobability P1 of potential failure), but that the potential failure isstill likely, failure identification and prevention tool 102 may usesecond machine learning algorithm 134 b to generate a second sequence ofresets to perform within computational environment 110 to providefurther improvement. This process may continue until the first machinelearning algorithm 134 a no longer predicts that a potential failure islikely to occur.

On the other hand, if, after executing the sequence of resets, failureidentification and prevention tool 102 determines that the computationalenvironment has not improved (e.g., a new probability P2 of potentialfailure is greater than or equal to the original probability P1), incertain embodiments, failure identification and prevention tool 102 maytransmit an alert 136 to a system administrator, requesting manualremediation of the system. In some embodiments, failure identificationand prevention tool 102 may be configured to generate a minimum numberof sequences of resets prior to transmitting alert 136.

III. Automatic Execution of Remediation Scripts to Prevent PotentialFailures

FIG. 3 presents a flowchart illustrating an example method 300(described in conjunction with elements of FIGS. 1 and 2 ) by whichfailure identification and prevention tool 102 automatically identifiespotential failures within computational environment 110 and attempts toprevent the identified failures by automatically identifying andexecuting an appropriate remediation script 126.

During process 302 failure identification and prevention tool 102divides the computational workload to be executed within computationalenvironment 110 (e.g., operating system workload 116, middlewareworkload 118, database workload 120, and application workload 122) intoa set of workload units 206 through 228. Failure identification andprevention tool 102 similarly divides the computational resources (e.g.,processing resources 112 and memory resources 114) of computationalenvironment 110 into a set of computational units 204 a through 204 x.Failure identification and prevention tool 102 the assigns each workloadunit to a given computational unit. Each computational unit isconfigured to execute its assigned workload unit as an independentexecution unit.

During process 304 failure identification and prevention tool 102measures a set of performance metrics for each independent executionunit (e.g., workload unit execution on a computational unit). Duringprocess 306 failure identification and prevention tool 102 uses themeasured performance metrics to predict a probability of a futurefailure within computational environment 110. For example, in certainembodiments, failure identification and prevention tool 102 appliesmachine learning algorithm 134 a to the measured performance metrics topredict a probability of a future failure within computationalenvironment 110. During process 308 failure identification andprevention tool 102 determines whether the probability of future failureis greater than a set threshold.

If, during process 308 failure identification and prevention tool 102determines that the probability of a failure within computationalenvironment 110 is greater than the set threshold, during process 310failure identification and prevention tool 102 determines whether thepredicted failure is a known failure or an unknown failure. For example,in certain embodiments, failure identification and prevention tool 102applies machine learning algorithm 134 b to the measured performancemetrics to classify the potential failure as one of a number of knownfailures, or an unknown failure. If, during process 310 failureidentification and prevention tool 102 determines that the potentialfailure is an unknown failure, during process 312 the tool transmits analert 136 to a system administrator 104, requesting that the systemadministrator perform manual remediation of the potential failure. Incertain embodiments, alert 136 includes values of the performancemetrics measured by failure identification and prevention tool 102.During process 314 failure identification and prevention tool 102 mayreceive a remediation script 138 from system administrator 104,indicating that the system administrator has identified and remediatedthe known failure. Accordingly, failure identification and preventiontool 102 may store the new remediation script within database 124 andclassify the previously unknown failure as a known failure that may beaddressed using the new remediation script. In certain embodiments,failure identification and prevention tool 102 may additionally retrainmachine learning algorithm 134 a. For instance, failure identificationand prevention tool 102 may include the newly identified potentialfailure as a new known failure of the set of known failures into whichmachine learning algorithm 134 a is configured to classify a potentialfailure.

If, during process 310 failure identification and prevention tool 102determines that the potential failure is a known failure, during process316 the tool accesses database 124 and identifies the remediation script126 that corresponds to the known failure. During process 318 failureidentification and prevention tool 102 executes the remediation scriptwithin computational environment 110 to prevent the potential failurefrom occurring within the computational environment. Method 300 may berepeated any number of times within system 100.

Modifications, additions, or omissions may be made to method 300depicted in FIG. 3 . Method 300 may include more, fewer, or other steps.For example, steps may be performed in parallel or in any suitableorder. While discussed as failure identification and prevention tool 102(or components thereof) performing certain steps, any suitablecomponents of system 100, including, for example, device 106, mayperform one or more steps of the method.

IV. Automatic Remediation of Unknown Potential Failures

FIG. 4 presents a flowchart illustrating an example method 400(described in conjunction with elements of FIGS. 1 and 2 ) by whichfailure identification and prevention tool 102 automatically identifiespotential failures within computational environment 110 and attempts toprevents to automatically remediate the identified failures.

During process 402 failure identification and prevention tool 102divides the computational workload to be executed within computationalenvironment 110 (e.g., operating system workload 116, middlewareworkload 118, database workload 120, application workload 122, etc.)into a set of N workload units. During process 404 failureidentification and prevention tool 102 divides the computationalresources (e.g., processing resources 112 and memory resources 114) ofcomputation environment 110 into a set of N×R computational units, whereN is the number of workload units, and R is the desired redundancylevel. In particular, failure identification and prevention tool 102divides the computational resources into a set of active computationalunits 202 a, and (R-1) sets of redundant computational units 202 b.During process 406 failure identification and prevention tool 102assigns each workload unit to an active computational unit and to (R-1)redundant computational units. During process 408 failure identificationand prevention tool 102 measures a set of performance metrics for eachactive computational unit.

During process 410 failure identification and prevention tool 102determines, based on the measured values for the performance metrics,whether a potential failure is likely to occur within computationalenvironment 110. For example, in certain embodiments, failureidentification and prevention tool 102 applies a machine learningalgorithm 134 a to the measured values for the performance metrics todetermine a probability of a potential failure within computationalenvironment 110, and compares that probability to a set threshold. Ifthe probability is greater than the set threshold, failureidentification and prevention tool 102 determines that the potentialfailure is likely to occur. If, during process 410 failureidentification and prevention tool 102 determines that a potentialfailure is not likely to occur, method 400 returns to process 408, wherethe tool continues to monitor computational environment 110 by measuringperformance metrics.

If, during process 410 failure identification and prevention tool 102determines that a potential failure is likely to occur withincomputational environment 110, during process 412 failure identificationand prevention tool 102 next determines whether the predicted failure isa known failure of a number of known failures, or an unknown failure.For example, in certain embodiments, failure identification andprevention tool 102 applies machine learning algorithm 134 b to themeasured performance metrics to classify the potential failure as one ofa number of known failures, or an unknown failure. If, during process412 failure identification and prevention tool 102 determines that thepotential failure is a known failure, during process 414 the toolaccesses database 124 and identifies the remediation script 126 thatcorresponds to the known failure. During process 416 failureidentification and prevention tool 102 executes the remediation scriptwithin computational environment 110 to prevent the potential failurefrom occurring within the computational environment.

If, during process 412 failure identification and prevention tool 102determines that the potential failure is an unknown failure, duringprocess 418 the tool applies second machine learning algorithm 134 b tothe values of the performance metrics, to generate a sequence of resetsto execute within computational environment 110. As explained above, inthe discussion of FIGS. 2A and 2B, each reset is associated with a givenworkload unit executing within computational environment 110, andcorresponds to a transfer of active execution of the given workload unitfrom an active computational unit to a redundant computational unit,followed by a reset of the active computational unit. During process 420failure identification and prevention tool 102 executes the sequence ofresets within computational environment 110.

During process 422 failure identification and prevention tool 102measures new values of the performance metrics for the set of activecomputational units (e.g., those computational units actively executingworkload units, which may include formerly redundant computational unitsthat have been converted into active computational units throughexecution of a reset within computational environment 110). Duringprocess 428 failure identification and prevention tool 102 appliesmachine learning algorithm 134 a to the new values of the performancemetrics to determine a new probability that a failure will occur withinthe computational environment, and determines whether the newprobability is greater than the set threshold. If, during process 428failure identification and prevention tool 102 determines that the newprobability is not greater than the set threshold, in certainembodiments, the tool rewards machine learning algorithm 134 b.

If, during process 428 failure identification and prevention tool 102determines that the new probability is greater than the set threshold,during process 424 failure identification and prevention tool 102determines whether the new probability is lower than the previouslydetermined probability. If, during process 424 failure identificationand prevention tool 102 determines that the new probability is lowerthan the previously determined probability, in certain embodiments,failure identification and prevention tool 102 rewards machine learningalgorithm 134 b, and method 400 returns to process 418, during whichfailure identification and prevention tool 102 generates a new sequenceof resets.

If, during process 424 failure identification and prevention tool 102determines that the new probability is greater than or equal to thepreviously determined probability, in certain embodiments, failureidentification and prevention tool 102 punishes machine learningalgorithm 134 b. During process 426 failure identification andprevention tool 102 transmits a request 136 to a system administrator104 requesting that the system administrator manually address thepotential failure.

Modifications, additions, or omissions may be made to method 400depicted in FIG. 4 . Method 400 may include more, fewer, or other steps.For example, steps may be performed in parallel or in any suitableorder. While discussed as failure identification and prevention tool 102(or components thereof) performing certain steps, any suitablecomponents of system 100, including, for example, device 106, mayperform one or more steps of the method.

Although the present disclosure includes several embodiments, a myriadof changes, variations, alterations, transformations, and modificationsmay be suggested to one skilled in the art, and it is intended that thepresent disclosure encompass such changes, variations, alterations,transformations, and modifications as falling within the scope of theappended claims.

What is claimed is:
 1. A system comprising: a computer system comprisingsystem resources configured to execute a workload, the workload dividedinto a plurality of workload units, the system resources comprising aplurality of units of system resources, each unit of system resourcesconfigured to execute a corresponding workload unit; a memory configuredto store a set of remediation scripts, each remediation scriptassociated with a known failure associated with the computer system andconfigured, when executed, to remediate the known failure; and ahardware processor communicatively coupled to the memory, the hardwareprocessor configured to: measure first values for a set of performancemetrics associated with the computer system, the set of performancemetrics comprising, for each unit of system resources, one or moremetrics associated with a performance of the unit of system resourcesand one or more metrics associated with a performance of the workloadunit executing on the unit of system resources; determine, based on thefirst values for the set of performance metrics, that a firstprobability that a first failure associated with the computer systemwill occur within a first future time is greater than a set threshold;and in response to determining that the first probability is greaterthan the set threshold: determine, based on the first values for the setof metrics, that the first failure is a known failure associated withthe computer system; and in response to determining that the firstfailure is the known failure, execute the remediation script associatedwith the known failure; measure second values for the set of parametersassociated with the computer system, the second values measured at adifferent time than the first values for the set of performance metrics;determine, based on the second values for the set of parameters, that asecond probability that a second failure associated with the computersystem will occur within a second future time is treater than the liventhreshold; and in response to determining that the second probability isgreater than the given threshold: determine, based on the second valuesfor the set of parameters, that the second failure is an unknownfailure; and in response to determining that the second failure is theunknown failure, transmit a message to a system administrator, themessage comprising the second values for the set of parameters; inresponse to transmitting the message to the system administrator:receive a new remediation script associated with a new known failure,the new known failure corresponding to the unknown failure; and storethe new remediation script in the memory, the new remediation scriptassociated with the new known failure.
 2. The system of claim 1,wherein: the workload comprises: an operating system component dividedinto a plurality of operating system units; a middleware componentdivided into a plurality of middleware units; and an applicationcomponent divided into a plurality of application units; and theplurality of workload units comprises the plurality of operating systemunits, the plurality of middleware units, and the plurality ofapplication units.
 3. The system of claim 1, wherein determining, basedon the first values for the set of performance metrics, that the firstprobability that the first failure associated with the computer systemwill occur within the first future time is greater than the giventhreshold comprises applying a machine learning algorithm to the firstvalues for the set of performance metrics, the machine learningalgorithm trained to identify correlations between values for the set ofparameters and failures associated with the computer system.
 4. Thesystem of claim 1, wherein determining, based on the first values forthe set of performance metrics, that the first failure is the knownfailure associated with the computer system comprises applying a machinelearning algorithm to the first values for the set of performancemetrics, the machine learning algorithm trained to assign a category ofa set of categories, based on values for the set of parameters, the setof categories: for each known failure, a category corresponding to theknown failure; and a category associated with unknown failures.
 5. Thesystem of claim 1, wherein: each unit of system resources of theplurality of units of system resources comprises at least one of a unitof processing resources and a unit of memory resources; and for eachunit of system resources of the plurality of units of system resources:the one or more metrics associated with the performance of the unit ofsystem resources comprises at least one of: a metric associated with aCPU usage of the unit of system resources; and a metric associated witha memory usage of the unit of system resources; and the one or moremetrics associated with the performance of the workload unit executingon the unit of system resources comprises at least one of: a metricassociated with any exceptions generated by the workload unit executingon the unit of system resources; and a metric associated with any errorcodes generated by the workload unit executing on the unit of systemresources.
 6. A method comprising: forming, from a computer systemcomprising system resources configured to execute a workload, aplurality of units of system resources; dividing the workload into aplurality of workload units, each workload unit of the plurality ofworkload assigned to a unit of system resources of the plurality ofunits of system resources; for each workload unit, executing theworkload unit on the assigned unit of system resources; measuring firstvalues for a set of performance metrics associated with the computersystem, the set of performance metrics comprising, for each unit ofsystem resources of the plurality of units of system resources, one ormore metrics associated with a performance of the unit of systemresources and one or more metrics associated with a performance of theworkload unit executing on the unit of system resources; determining,based on the first values for the set of performance metrics, that afirst probability that a first failure associated with the computersystem will occur within a first future time is greater than a setthreshold; and in response to determining that the first probability isgreater than the set threshold: determining, based on the first valuesfor the set of performance metrics, that the first failure is a knownfailure associated with the computer system; identifying a remediationscript associated with the known failure from a set of remediationscripts stored within a memory, the remediation script configured, whenexecuted, to remediate the known failure; and executing the identifiedremediation script; measuring second values for the set of parametersassociated with the computer system, the second values measured at adifferent time than the first values for the set of performance metrics;determining, based on the second values for the set of parameters, thata second probability that a second failure associated with the computersystem will occur within a second future time is greater than the giventhreshold; and in response to determining that the second probability isgreater than the given threshold: determining, based on the secondvalues for the set of parameters, that the second failure is an unknownfailure; and in response to determining that the second failure is theunknown failure, transmitting a message to a system administrator, themessage comprising the second values for the set of parameters; inresponse to transmitting the message to the system administrator:receiving a new remediation script associated with a new known failure,the new known failure corresponding to the unknown failure; and storingthe new remediation script in the memory, the new remediation scriptassociated with the new known failure.
 7. The method of claim 6,wherein: the workload comprises: an operating system component dividedinto a plurality of operating system units; a middleware componentdivided into a plurality of middleware units; and an applicationcomponent divided into a plurality of application units; and theplurality of workload units comprises the plurality of operating systemunits, the plurality of middleware units, and the plurality ofapplication units.
 8. The method of claim 6, wherein determining, basedon the first values for the set of performance metrics, that the firstprobability that the first failure associated with the computer systemwill occur within the first future time is greater than the giventhreshold comprises applying a machine learning algorithm to the firstvalues for the set of performance metrics, the machine learningalgorithm trained to identify correlations between values for the set ofparameters and failures associated with the computer system.
 9. Themethod of claim 6, wherein determining, based on the first values forthe set of performance metrics, that the first failure is the knownfailure associated with the computer system comprises applying a machinelearning algorithm to the first values for the set of performancemetrics, the machine learning algorithm trained to assign a category ofa set of categories, based on values for the set of parameters, the setof categories comprising: for each known failure of the set of knownfailures, a category corresponding to the known failure; and a categoryassociated with unknown failures.
 10. The method of claim 6, wherein:each unit of system resources of the plurality of units of systemresources comprises at least one of a unit of processing resources and aunit of memory resources; and for each unit of system resources of theplurality of units of system resources: the one or more metricsassociated with the performance of the unit of system resourcescomprises at least one of: a metric associated with a CPU usage of theunit of system resources; and a metric associated with a memory usage ofthe unit of system resources; and the one or more metrics associatedwith the performance of the workload unit executing on the unit ofsystem resources comprises at least one of: a metric associated with anyexceptions generated by the workload unit executing on the unit ofsystem resources; and a metric associated with any error codes generatedby the workload unit executing on the unit of system resources.
 11. Asystem comprising: a computer system configured to execute a workload,the workload divided into a plurality of workload units, the computersystem comprising a plurality of units of system resources, each unit ofsystem resources of the plurality of units of system resourcesconfigured to execute a workload unit of the plurality of workloadunits; a memory configured to store a set of remediation scripts, eachremediation script associated with a known failure associated with thecomputer system and configured, when executed, to remediate the knownfailure; and a hardware processor communicatively coupled to the memory,the hardware processor configured to: measure first values for a set ofperformance metrics associated with the computer system, the set ofperformance metrics comprising, for each unit of system resources of theplurality of units of system resources, one or more metrics associatedwith a performance of the unit of system resources and one or moremetrics associated with a performance of the workload unit executing onthe unit of system resources; determine, based on the first values forthe set of performance metrics, that a first probability that a firstfailure associated with the computer system will occur within a firstfuture time is greater than a set threshold; and in response todetermining that the first probability is greater than the setthreshold: determine, based on the first values for the set of metrics,that the first failure is an unknown failure; and in response todetermining that the first failure is the unknown failure, transmit amessage to a system administrator, the message comprising the firstvalues for the set of parameters; in response to transmitting themessage to the system administrator, the hardware processor is furtherconfigured to: receive a new remediation script associated with a newknown failure, the new known failure corresponding to the unknownfailure; store the new remediation script in the memory, the newremediation script associated with the new known failure.
 12. The systemof claim 11, wherein: the workload comprises: an operating systemcomponent divided into a plurality of operating system units; amiddleware component divided into a plurality of middleware units; andan application component divided into a plurality of application units;and the plurality of workload units comprises the plurality of operatingsystem units, the plurality of middleware units, and the plurality ofapplication units.
 13. The system of claim 11, wherein determining,based on the first values for the set of parameters, that the firstprobability that the first failure associated with the computer systemwill occur within the first future time is greater than the giventhreshold comprises applying a machine learning algorithm to the firstvalues for the set of parameters, the machine learning algorithm trainedto identify correlations between values for the set of parameters andfailures associated with the computer system.
 14. The system of claim11, wherein determining, based on the first values for the set ofparameters, that the first failure is the known failure associated withthe computer system comprises applying a machine learning algorithm tothe first values for the set of parameters, the machine learningalgorithm trained to assign a category of a set of categories, based onvalues for the set of parameters, the set of categories: for each knownfailure of the set of known failures, a category corresponding to theknown failure; and a category associated with unknown failures.
 15. Thesystem of claim 11, wherein: each unit of system resources of theplurality of units of system resources comprises at least one of a unitof processing resources and a unit of memory resources; and for eachunit of system resources of the plurality of units of system resources:the one or more metrics associated with the performance of the unit ofsystem resources comprises at least one of: a metric associated with aCPU usage of the unit of system resources; and a metric associated witha memory usage of the unit of system resources; and the one or moremetrics associated with the performance of the workload unit executingon the unit of system resources comprises at least one of: a metricassociated with any exceptions generated by the workload unit executingon the unit of system resources; and a metric associated with any errorcodes generated by the workload unit executing on the unit of systemresources.